Experimenters in the health sciences often analyze their data as a General Linear Model (GLM; fixed effects and analysis of variance, analysis of covariance, and simple and multiple regression analyses are all subcases of this model). As part of this analysis, simultaneous confidence intervals for several quantities (e.g. all differences between treatments or between treatments and a control) are often necessary, or would be useful. An exact, fully general method for computing these intervals has recently been developed. The method is based on computer simulation. Except for a few restricted, carefully balanced subcases of the GLM, competing methods are very conservative, wasting financial resources, time, and in a health-related study, lives. Preliminary results suggest that the simulation-based method is very efficient and stable over repeated simulations. The proposed research will develop, thoroughly test, and, if appropriate, publicize a computer program to compute the intervals. The testing will be done both with case studies and extensive Monte Carlo simulations.